Overview

Dataset statistics

Number of variables42
Number of observations25192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 MiB
Average record size in memory336.0 B

Variable types

Numeric27
Categorical15

Warnings

num_outbound_cmds has constant value "0" Constant
is_host_login has constant value "0" Constant
service has a high cardinality: 66 distinct values High cardinality
hot is highly correlated with is_guest_loginHigh correlation
logged_in is highly correlated with count and 3 other fieldsHigh correlation
num_compromised is highly correlated with su_attempted and 2 other fieldsHigh correlation
root_shell is highly correlated with su_attemptedHigh correlation
su_attempted is highly correlated with num_compromised and 3 other fieldsHigh correlation
num_root is highly correlated with num_compromised and 2 other fieldsHigh correlation
num_access_files is highly correlated with num_compromised and 2 other fieldsHigh correlation
is_guest_login is highly correlated with hotHigh correlation
count is highly correlated with logged_in and 1 other fieldsHigh correlation
serror_rate is highly correlated with srv_serror_rate and 5 other fieldsHigh correlation
srv_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with logged_in and 8 other fieldsHigh correlation
dst_host_count is highly correlated with same_srv_rate and 1 other fieldsHigh correlation
dst_host_srv_count is highly correlated with logged_in and 6 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with logged_in and 7 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
src_bytes is highly correlated with dst_bytes and 11 other fieldsHigh correlation
dst_bytes is highly correlated with src_bytes and 9 other fieldsHigh correlation
hot is highly correlated with is_guest_loginHigh correlation
logged_in is highly correlated with src_bytes and 7 other fieldsHigh correlation
root_shell is highly correlated with su_attemptedHigh correlation
su_attempted is highly correlated with root_shellHigh correlation
is_guest_login is highly correlated with hotHigh correlation
count is highly correlated with src_bytes and 11 other fieldsHigh correlation
srv_count is highly correlated with countHigh correlation
serror_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
srv_serror_rate is highly correlated with src_bytes and 8 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with src_bytes and 13 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 12 other fieldsHigh correlation
dst_host_count is highly correlated with count and 5 other fieldsHigh correlation
dst_host_srv_count is highly correlated with src_bytes and 8 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with src_bytes and 12 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with count and 3 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with count and 3 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
duration is highly correlated with land and 23 other fieldsHigh correlation
src_bytes is highly correlated with land and 25 other fieldsHigh correlation
dst_bytes is highly correlated with land and 25 other fieldsHigh correlation
land is highly correlated with duration and 24 other fieldsHigh correlation
wrong_fragment is highly correlated with duration and 27 other fieldsHigh correlation
urgent is highly correlated with duration and 24 other fieldsHigh correlation
hot is highly correlated with duration and 27 other fieldsHigh correlation
num_failed_logins is highly correlated with duration and 24 other fieldsHigh correlation
logged_in is highly correlated with land and 23 other fieldsHigh correlation
num_compromised is highly correlated with duration and 24 other fieldsHigh correlation
root_shell is highly correlated with duration and 28 other fieldsHigh correlation
su_attempted is highly correlated with duration and 24 other fieldsHigh correlation
num_root is highly correlated with duration and 28 other fieldsHigh correlation
num_file_creations is highly correlated with duration and 24 other fieldsHigh correlation
num_shells is highly correlated with duration and 28 other fieldsHigh correlation
num_access_files is highly correlated with duration and 24 other fieldsHigh correlation
num_outbound_cmds is highly correlated with duration and 28 other fieldsHigh correlation
is_host_login is highly correlated with duration and 24 other fieldsHigh correlation
is_guest_login is highly correlated with duration and 28 other fieldsHigh correlation
count is highly correlated with rerror_rate and 7 other fieldsHigh correlation
srv_count is highly correlated with serror_rate and 7 other fieldsHigh correlation
serror_rate is highly correlated with duration and 19 other fieldsHigh correlation
srv_serror_rate is highly correlated with duration and 19 other fieldsHigh correlation
rerror_rate is highly correlated with duration and 27 other fieldsHigh correlation
srv_rerror_rate is highly correlated with duration and 27 other fieldsHigh correlation
same_srv_rate is highly correlated with land and 18 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 16 other fieldsHigh correlation
srv_diff_host_rate is highly correlated with duration and 21 other fieldsHigh correlation
dst_host_count is highly correlated with src_bytes and 16 other fieldsHigh correlation
dst_host_srv_count is highly correlated with dst_host_same_srv_rate and 5 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with serror_rate and 8 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with src_bytes and 16 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with count and 7 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with land and 19 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with duration and 19 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with duration and 19 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with duration and 33 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with duration and 35 other fieldsHigh correlation
num_access_files is highly correlated with su_attempted and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with dst_host_srv_serror_rate and 12 other fieldsHigh correlation
srv_count is highly correlated with count and 2 other fieldsHigh correlation
duration is highly correlated with dst_host_diff_srv_rateHigh correlation
dst_host_same_src_port_rate is highly correlated with dst_host_srv_count and 6 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with same_srv_rate and 10 other fieldsHigh correlation
su_attempted is highly correlated with num_access_files and 2 other fieldsHigh correlation
dst_host_srv_count is highly correlated with same_srv_rate and 11 other fieldsHigh correlation
flag is highly correlated with same_srv_rate and 14 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with dst_host_same_src_port_rate and 3 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with same_srv_rate and 12 other fieldsHigh correlation
is_guest_login is highly correlated with hot and 1 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with same_srv_rate and 12 other fieldsHigh correlation
serror_rate is highly correlated with same_srv_rate and 11 other fieldsHigh correlation
hot is highly correlated with is_guest_login and 1 other fieldsHigh correlation
count is highly correlated with same_srv_rate and 12 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with flag and 5 other fieldsHigh correlation
num_root is highly correlated with num_access_files and 3 other fieldsHigh correlation
srv_serror_rate is highly correlated with same_srv_rate and 10 other fieldsHigh correlation
srv_rerror_rate is highly correlated with flag and 3 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with flag and 3 other fieldsHigh correlation
service is highly correlated with same_srv_rate and 19 other fieldsHigh correlation
rerror_rate is highly correlated with flag and 7 other fieldsHigh correlation
dst_host_count is highly correlated with same_srv_rate and 6 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with duration and 5 other fieldsHigh correlation
num_compromised is highly correlated with num_access_files and 3 other fieldsHigh correlation
logged_in is highly correlated with same_srv_rate and 11 other fieldsHigh correlation
diff_srv_rate is highly correlated with same_srv_rate and 2 other fieldsHigh correlation
srv_diff_host_rate is highly correlated with dst_host_srv_diff_host_rate and 1 other fieldsHigh correlation
class is highly correlated with same_srv_rate and 11 other fieldsHigh correlation
root_shell is highly correlated with num_root and 1 other fieldsHigh correlation
protocol_type is highly correlated with srv_count and 3 other fieldsHigh correlation
urgent is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
logged_in is highly correlated with flag and 4 other fieldsHigh correlation
flag is highly correlated with logged_in and 3 other fieldsHigh correlation
class is highly correlated with logged_in and 4 other fieldsHigh correlation
num_failed_logins is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
root_shell is highly correlated with su_attempted and 2 other fieldsHigh correlation
protocol_type is highly correlated with service and 2 other fieldsHigh correlation
num_shells is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
su_attempted is highly correlated with root_shell and 2 other fieldsHigh correlation
service is highly correlated with logged_in and 5 other fieldsHigh correlation
num_outbound_cmds is highly correlated with urgent and 13 other fieldsHigh correlation
land is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
is_host_login is highly correlated with urgent and 13 other fieldsHigh correlation
wrong_fragment is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
is_guest_login is highly correlated with service and 2 other fieldsHigh correlation
src_bytes is highly skewed (γ1 = 157.5585416) Skewed
dst_bytes is highly skewed (γ1 = 54.77757621) Skewed
num_compromised is highly skewed (γ1 = 62.19108752) Skewed
num_root is highly skewed (γ1 = 62.3210635) Skewed
num_file_creations is highly skewed (γ1 = 52.1416881) Skewed
num_access_files is highly skewed (γ1 = 41.75276352) Skewed
duration has 23168 (92.0%) zeros Zeros
src_bytes has 9866 (39.2%) zeros Zeros
dst_bytes has 13574 (53.9%) zeros Zeros
hot has 24672 (97.9%) zeros Zeros
num_compromised has 24920 (98.9%) zeros Zeros
num_root has 25058 (99.5%) zeros Zeros
num_file_creations has 25126 (99.7%) zeros Zeros
num_access_files has 25113 (99.7%) zeros Zeros
serror_rate has 17329 (68.8%) zeros Zeros
srv_serror_rate has 17708 (70.3%) zeros Zeros
rerror_rate has 21985 (87.3%) zeros Zeros
srv_rerror_rate has 21959 (87.2%) zeros Zeros
same_srv_rate has 543 (2.2%) zeros Zeros
diff_srv_rate has 15245 (60.5%) zeros Zeros
srv_diff_host_rate has 19517 (77.5%) zeros Zeros
dst_host_same_srv_rate has 1379 (5.5%) zeros Zeros
dst_host_diff_srv_rate has 9343 (37.1%) zeros Zeros
dst_host_same_src_port_rate has 12673 (50.3%) zeros Zeros
dst_host_srv_diff_host_rate has 17387 (69.0%) zeros Zeros
dst_host_serror_rate has 16221 (64.4%) zeros Zeros
dst_host_srv_serror_rate has 17005 (67.5%) zeros Zeros
dst_host_rerror_rate has 20688 (82.1%) zeros Zeros
dst_host_srv_rerror_rate has 21349 (84.7%) zeros Zeros

Reproduction

Analysis started2022-04-05 01:03:07.356439
Analysis finished2022-04-05 01:14:12.884702
Duration11 minutes and 5.53 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct758
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.0541045
Minimum0
Maximum42862
Zeros23168
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:13.037922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum42862
Range42862
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2686.55564
Coefficient of variation (CV)8.806816891
Kurtosis146.7010243
Mean305.0541045
Median Absolute Deviation (MAD)0
Skewness11.53264335
Sum7684923
Variance7217581.207
MonotonicityNot monotonic
2022-04-04T19:14:13.225398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023168
92.0%
1374
 
1.5%
2165
 
0.7%
3102
 
0.4%
475
 
0.3%
562
 
0.2%
641
 
0.2%
2740
 
0.2%
2838
 
0.2%
726
 
0.1%
Other values (748)1101
 
4.4%
ValueCountFrequency (%)
023168
92.0%
1374
 
1.5%
2165
 
0.7%
3102
 
0.4%
475
 
0.3%
562
 
0.2%
641
 
0.2%
726
 
0.1%
819
 
0.1%
922
 
0.1%
ValueCountFrequency (%)
428621
< 0.1%
426581
< 0.1%
426361
< 0.1%
424701
< 0.1%
422601
< 0.1%
420211
< 0.1%
418021
< 0.1%
415611
< 0.1%
415411
< 0.1%
414761
< 0.1%

protocol_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
tcp
20526 
udp
3011 
icmp
 
1655

Length

Max length4
Median length3
Mean length3.065695459
Min length3

Characters and Unicode

Total characters77231
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowudp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp20526
81.5%
udp3011
 
12.0%
icmp1655
 
6.6%

Length

2022-04-04T19:14:13.555452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:13.651840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp20526
81.5%
udp3011
 
12.0%
icmp1655
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p25192
32.6%
c22181
28.7%
t20526
26.6%
u3011
 
3.9%
d3011
 
3.9%
i1655
 
2.1%
m1655
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter77231
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p25192
32.6%
c22181
28.7%
t20526
26.6%
u3011
 
3.9%
d3011
 
3.9%
i1655
 
2.1%
m1655
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin77231
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p25192
32.6%
c22181
28.7%
t20526
26.6%
u3011
 
3.9%
d3011
 
3.9%
i1655
 
2.1%
m1655
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII77231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p25192
32.6%
c22181
28.7%
t20526
26.6%
u3011
 
3.9%
d3011
 
3.9%
i1655
 
2.1%
m1655
 
2.1%

service
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
http
8003 
private
4351 
domain_u
1820 
smtp
1449 
ftp_data
1396 
Other values (61)
8173 

Length

Max length11
Median length5
Mean length5.473682121
Min length3

Characters and Unicode

Total characters137893
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowftp_data
2nd rowother
3rd rowprivate
4th rowhttp
5th rowhttp

Common Values

ValueCountFrequency (%)
http8003
31.8%
private4351
17.3%
domain_u1820
 
7.2%
smtp1449
 
5.8%
ftp_data1396
 
5.5%
eco_i909
 
3.6%
other858
 
3.4%
ecr_i613
 
2.4%
telnet483
 
1.9%
finger366
 
1.5%
Other values (56)4944
19.6%

Length

2022-04-04T19:14:14.017191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http8003
31.8%
private4351
17.3%
domain_u1820
 
7.2%
smtp1449
 
5.8%
ftp_data1396
 
5.5%
eco_i909
 
3.6%
other858
 
3.4%
ecr_i613
 
2.4%
telnet483
 
1.9%
finger366
 
1.5%
Other values (56)4944
19.6%

Most occurring characters

ValueCountFrequency (%)
t29049
21.1%
p17578
12.7%
a10319
 
7.5%
e9860
 
7.2%
h9852
 
7.1%
i9744
 
7.1%
r6989
 
5.1%
_5928
 
4.3%
o4909
 
3.6%
n4525
 
3.3%
Other values (29)29140
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter130368
94.5%
Connector Punctuation5928
 
4.3%
Decimal Number1283
 
0.9%
Uppercase Letter314
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t29049
22.3%
p17578
13.5%
a10319
 
7.9%
e9860
 
7.6%
h9852
 
7.6%
i9744
 
7.5%
r6989
 
5.4%
o4909
 
3.8%
n4525
 
3.5%
v4458
 
3.4%
Other values (15)23085
17.7%
Decimal Number
ValueCountFrequency (%)
4364
28.4%
3338
26.3%
0174
13.6%
9172
13.4%
5172
13.4%
145
 
3.5%
217
 
1.3%
81
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
Z172
54.8%
I40
 
12.7%
R40
 
12.7%
C40
 
12.7%
X22
 
7.0%
Connector Punctuation
ValueCountFrequency (%)
_5928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin130682
94.8%
Common7211
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t29049
22.2%
p17578
13.5%
a10319
 
7.9%
e9860
 
7.5%
h9852
 
7.5%
i9744
 
7.5%
r6989
 
5.3%
o4909
 
3.8%
n4525
 
3.5%
v4458
 
3.4%
Other values (20)23399
17.9%
Common
ValueCountFrequency (%)
_5928
82.2%
4364
 
5.0%
3338
 
4.7%
0174
 
2.4%
9172
 
2.4%
5172
 
2.4%
145
 
0.6%
217
 
0.2%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII137893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t29049
21.1%
p17578
12.7%
a10319
 
7.5%
e9860
 
7.2%
h9852
 
7.1%
i9744
 
7.1%
r6989
 
5.1%
_5928
 
4.3%
o4909
 
3.6%
n4525
 
3.3%
Other values (29)29140
21.1%

flag
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
SF
14973 
S0
7009 
REJ
2216 
RSTR
 
497
RSTO
 
304
Other values (6)
 
193

Length

Max length6
Median length2
Mean length2.155088917
Min length2

Characters and Unicode

Total characters54291
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowS0
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF14973
59.4%
S07009
27.8%
REJ2216
 
8.8%
RSTR497
 
2.0%
RSTO304
 
1.2%
S188
 
0.3%
SH43
 
0.2%
RSTOS021
 
0.1%
S221
 
0.1%
S315
 
0.1%

Length

2022-04-04T19:14:14.354986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf14973
59.4%
s07009
27.8%
rej2216
 
8.8%
rstr497
 
2.0%
rsto304
 
1.2%
s188
 
0.3%
sh43
 
0.2%
rstos021
 
0.1%
s221
 
0.1%
s315
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S22992
42.3%
F14973
27.6%
07030
 
12.9%
R3535
 
6.5%
E2216
 
4.1%
J2216
 
4.1%
T827
 
1.5%
O330
 
0.6%
188
 
0.2%
H48
 
0.1%
Other values (2)36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter47137
86.8%
Decimal Number7154
 
13.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S22992
48.8%
F14973
31.8%
R3535
 
7.5%
E2216
 
4.7%
J2216
 
4.7%
T827
 
1.8%
O330
 
0.7%
H48
 
0.1%
Decimal Number
ValueCountFrequency (%)
07030
98.3%
188
 
1.2%
221
 
0.3%
315
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin47137
86.8%
Common7154
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S22992
48.8%
F14973
31.8%
R3535
 
7.5%
E2216
 
4.7%
J2216
 
4.7%
T827
 
1.8%
O330
 
0.7%
H48
 
0.1%
Common
ValueCountFrequency (%)
07030
98.3%
188
 
1.2%
221
 
0.3%
315
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII54291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S22992
42.3%
F14973
27.6%
07030
 
12.9%
R3535
 
6.5%
E2216
 
4.1%
J2216
 
4.1%
T827
 
1.5%
O330
 
0.6%
188
 
0.2%
H48
 
0.1%
Other values (2)36
 
0.1%

src_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1665
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24330.62822
Minimum0
Maximum381709090
Zeros9866
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:14.535193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3279
95-th percentile1486.45
Maximum381709090
Range381709090
Interquartile range (IQR)279

Descriptive statistics

Standard deviation2410805.402
Coefficient of variation (CV)99.08520983
Kurtosis24944.61428
Mean24330.62822
Median Absolute Deviation (MAD)44
Skewness157.5585416
Sum612937186
Variance5.811982686 × 1012
MonotonicityNot monotonic
2022-04-04T19:14:14.736862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09866
39.2%
8738
 
2.9%
1480
 
1.9%
44467
 
1.9%
45416
 
1.7%
1032390
 
1.5%
46284
 
1.1%
43231
 
0.9%
147210
 
0.8%
105204
 
0.8%
Other values (1655)11906
47.3%
ValueCountFrequency (%)
09866
39.2%
1480
 
1.9%
41
 
< 0.1%
54
 
< 0.1%
632
 
0.1%
726
 
0.1%
8738
 
2.9%
939
 
0.2%
1032
 
0.1%
1114
 
0.1%
ValueCountFrequency (%)
3817090901
 
< 0.1%
76658761
 
< 0.1%
72485521
 
< 0.1%
51356783
 
< 0.1%
51338768
 
< 0.1%
51314242
 
< 0.1%
50974721
 
< 0.1%
22803181
 
< 0.1%
21946202
 
< 0.1%
219461944
0.2%

dst_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3922
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3491.847174
Minimum0
Maximum5151385
Zeros13574
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:14.941411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3530.25
95-th percentile8314
Maximum5151385
Range5151385
Interquartile range (IQR)530.25

Descriptive statistics

Standard deviation88830.71833
Coefficient of variation (CV)25.43946339
Kurtosis3130.172645
Mean3491.847174
Median Absolute Deviation (MAD)0
Skewness54.77757621
Sum87966614
Variance7890896519
MonotonicityNot monotonic
2022-04-04T19:14:15.141772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013574
53.9%
105309
 
1.2%
8314175
 
0.7%
44115
 
0.5%
42105
 
0.4%
330105
 
0.4%
332103
 
0.4%
33197
 
0.4%
494
 
0.4%
32988
 
0.3%
Other values (3912)10427
41.4%
ValueCountFrequency (%)
013574
53.9%
16
 
< 0.1%
494
 
0.4%
157
 
< 0.1%
177
 
< 0.1%
183
 
< 0.1%
242
 
< 0.1%
263
 
< 0.1%
282
 
< 0.1%
298
 
< 0.1%
ValueCountFrequency (%)
51513851
< 0.1%
51508361
< 0.1%
51507721
< 0.1%
51501801
< 0.1%
51495331
< 0.1%
51314241
< 0.1%
51299641
< 0.1%
16394841
< 0.1%
15935801
< 0.1%
14370921
< 0.1%

land
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25190 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

Length

2022-04-04T19:14:15.462572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:15.553164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025190
> 99.9%
12
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24968 
3
 
187
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

Length

2022-04-04T19:14:15.814053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:15.914747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

Most occurring characters

ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024968
99.1%
3187
 
0.7%
137
 
0.1%

urgent
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25191 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

Length

2022-04-04T19:14:16.155002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:16.246372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025191
> 99.9%
11
 
< 0.1%

hot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19803906
Minimum0
Maximum77
Zeros24672
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:16.346043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.154201767
Coefficient of variation (CV)10.87766104
Kurtosis213.6979586
Mean0.19803906
Median Absolute Deviation (MAD)0
Skewness13.58953733
Sum4989
Variance4.640585254
MonotonicityNot monotonic
2022-04-04T19:14:16.500427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
024672
97.9%
2200
 
0.8%
178
 
0.3%
3055
 
0.2%
2852
 
0.2%
437
 
0.1%
626
 
0.1%
517
 
0.1%
2213
 
0.1%
249
 
< 0.1%
Other values (12)33
 
0.1%
ValueCountFrequency (%)
024672
97.9%
178
 
0.3%
2200
 
0.8%
37
 
< 0.1%
437
 
0.1%
517
 
0.1%
626
 
0.1%
72
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
771
 
< 0.1%
3055
0.2%
2852
0.2%
251
 
< 0.1%
249
 
< 0.1%
2213
 
0.1%
201
 
< 0.1%
198
 
< 0.1%
186
 
< 0.1%
171
 
< 0.1%

num_failed_logins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25169 
1
 
19
2
 
2
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

Length

2022-04-04T19:14:16.809643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:16.906350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025169
99.9%
119
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%

logged_in
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
15247 
1
9945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
015247
60.5%
19945
39.5%

Length

2022-04-04T19:14:17.157361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:17.248013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
015247
60.5%
19945
39.5%

Most occurring characters

ValueCountFrequency (%)
015247
60.5%
19945
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015247
60.5%
19945
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015247
60.5%
19945
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015247
60.5%
19945
39.5%

num_compromised
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2278501111
Minimum0
Maximum884
Zeros24920
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:17.353516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.41735204
Coefficient of variation (CV)45.7201973
Kurtosis4313.775786
Mean0.2278501111
Median Absolute Deviation (MAD)0
Skewness62.19108752
Sum5740
Variance108.5212235
MonotonicityNot monotonic
2022-04-04T19:14:17.504480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
024920
98.9%
1194
 
0.8%
221
 
0.1%
413
 
0.1%
68
 
< 0.1%
37
 
< 0.1%
55
 
< 0.1%
72
 
< 0.1%
1512
 
< 0.1%
122
 
< 0.1%
Other values (18)18
 
0.1%
ValueCountFrequency (%)
024920
98.9%
1194
 
0.8%
221
 
0.1%
37
 
< 0.1%
413
 
0.1%
55
 
< 0.1%
68
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
8841
< 0.1%
7891
< 0.1%
5581
< 0.1%
5201
< 0.1%
4621
< 0.1%
4571
< 0.1%
3711
< 0.1%
2171
< 0.1%
1931
< 0.1%
1571
< 0.1%

root_shell
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25153 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

Length

2022-04-04T19:14:17.790596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:17.880916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

Most occurring characters

ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025153
99.8%
139
 
0.2%

su_attempted
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25171 
2
 
13
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

Length

2022-04-04T19:14:18.126994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:18.218534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025171
99.9%
213
 
0.1%
18
 
< 0.1%

num_root
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2498412194
Minimum0
Maximum975
Zeros25058
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:18.330508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum975
Range975
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.5008418
Coefficient of variation (CV)46.03260352
Kurtosis4315.767476
Mean0.2498412194
Median Absolute Deviation (MAD)0
Skewness62.3210635
Sum6294
Variance132.269362
MonotonicityNot monotonic
2022-04-04T19:14:18.492824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
025058
99.5%
147
 
0.2%
924
 
0.1%
623
 
0.1%
210
 
< 0.1%
56
 
< 0.1%
42
 
< 0.1%
32
 
< 0.1%
141
 
< 0.1%
1001
 
< 0.1%
Other values (18)18
 
0.1%
ValueCountFrequency (%)
025058
99.5%
147
 
0.2%
210
 
< 0.1%
32
 
< 0.1%
42
 
< 0.1%
56
 
< 0.1%
623
 
0.1%
71
 
< 0.1%
924
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
9751
< 0.1%
8671
< 0.1%
6291
< 0.1%
5721
< 0.1%
5121
< 0.1%
5081
< 0.1%
4171
< 0.1%
2471
< 0.1%
1911
< 0.1%
1791
< 0.1%

num_file_creations
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01472689743
Minimum0
Maximum40
Zeros25126
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:18.646502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5296023002
Coefficient of variation (CV)35.96156643
Kurtosis3158.205144
Mean0.01472689743
Median Absolute Deviation (MAD)0
Skewness52.1416881
Sum371
Variance0.2804785964
MonotonicityNot monotonic
2022-04-04T19:14:18.796768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
025126
99.7%
137
 
0.1%
27
 
< 0.1%
43
 
< 0.1%
82
 
< 0.1%
182
 
< 0.1%
52
 
< 0.1%
211
 
< 0.1%
111
 
< 0.1%
201
 
< 0.1%
Other values (10)10
 
< 0.1%
ValueCountFrequency (%)
025126
99.7%
137
 
0.1%
27
 
< 0.1%
31
 
< 0.1%
43
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
82
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
401
< 0.1%
381
< 0.1%
291
< 0.1%
211
< 0.1%
201
< 0.1%
191
< 0.1%
182
< 0.1%
171
< 0.1%
151
< 0.1%
141
< 0.1%

num_shells
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25183 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

Length

2022-04-04T19:14:19.089501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:19.179030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025183
> 99.9%
19
 
< 0.1%

num_access_files
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004326770403
Minimum0
Maximum8
Zeros25113
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:19.265035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09852397678
Coefficient of variation (CV)22.7707892
Kurtosis2499.907798
Mean0.004326770403
Median Absolute Deviation (MAD)0
Skewness41.75276352
Sum109
Variance0.009706974
MonotonicityNot monotonic
2022-04-04T19:14:19.395892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
025113
99.7%
165
 
0.3%
28
 
< 0.1%
32
 
< 0.1%
52
 
< 0.1%
41
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
025113
99.7%
165
 
0.3%
28
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
52
 
< 0.1%
41
 
< 0.1%
32
 
< 0.1%
28
 
< 0.1%
165
 
0.3%
025113
99.7%

num_outbound_cmds
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025192
100.0%

Length

2022-04-04T19:14:19.676882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:19.772154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025192
100.0%

Most occurring characters

ValueCountFrequency (%)
025192
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025192
100.0%

is_host_login
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025192
100.0%

Length

2022-04-04T19:14:20.007871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:20.095692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
025192
100.0%

Most occurring characters

ValueCountFrequency (%)
025192
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025192
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025192
100.0%

is_guest_login
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24962 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25192
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

Length

2022-04-04T19:14:20.322266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:20.412057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

Most occurring characters

ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25192
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common25192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII25192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024962
99.1%
1230
 
0.9%

count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct466
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.59117974
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:20.531549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median14
Q3144
95-th percentile286
Maximum511
Range510
Interquartile range (IQR)142

Descriptive statistics

Standard deviation114.6734509
Coefficient of variation (CV)1.355619478
Kurtosis1.978018032
Mean84.59117974
Median Absolute Deviation (MAD)13
Skewness1.503732531
Sum2131021
Variance13150.00034
MonotonicityNot monotonic
2022-04-04T19:14:20.731400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15519
21.9%
21934
 
7.7%
3769
 
3.1%
4696
 
2.8%
5597
 
2.4%
6477
 
1.9%
7462
 
1.8%
8404
 
1.6%
9342
 
1.4%
11317
 
1.3%
Other values (456)13675
54.3%
ValueCountFrequency (%)
15519
21.9%
21934
 
7.7%
3769
 
3.1%
4696
 
2.8%
5597
 
2.4%
6477
 
1.9%
7462
 
1.8%
8404
 
1.6%
9342
 
1.4%
10311
 
1.2%
ValueCountFrequency (%)
511293
1.2%
51058
 
0.2%
50949
 
0.2%
5086
 
< 0.1%
5071
 
< 0.1%
5061
 
< 0.1%
5021
 
< 0.1%
5003
 
< 0.1%
4971
 
< 0.1%
4962
 
< 0.1%

srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct414
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.69875357
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:20.923460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q318
95-th percentile157
Maximum511
Range510
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.46824199
Coefficient of variation (CV)2.616299748
Kurtosis24.39669648
Mean27.69875357
Median Absolute Deviation (MAD)7
Skewness4.707522831
Sum697787
Variance5251.646097
MonotonicityNot monotonic
2022-04-04T19:14:21.127653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15080
20.2%
22538
 
10.1%
31223
 
4.9%
41086
 
4.3%
5913
 
3.6%
6849
 
3.4%
7800
 
3.2%
8751
 
3.0%
9718
 
2.9%
11688
 
2.7%
Other values (404)10546
41.9%
ValueCountFrequency (%)
15080
20.2%
22538
10.1%
31223
 
4.9%
41086
 
4.3%
5913
 
3.6%
6849
 
3.4%
7800
 
3.2%
8751
 
3.0%
9718
 
2.9%
10647
 
2.6%
ValueCountFrequency (%)
511200
0.8%
51036
 
0.1%
5096
 
< 0.1%
5082
 
< 0.1%
5001
 
< 0.1%
4971
 
< 0.1%
4961
 
< 0.1%
4922
 
< 0.1%
4891
 
< 0.1%
4881
 
< 0.1%

serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2863377263
Minimum0
Maximum1
Zeros17329
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:21.331382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4473123227
Coefficient of variation (CV)1.562184378
Kurtosis-1.07438947
Mean0.2863377263
Median Absolute Deviation (MAD)0
Skewness0.9526467377
Sum7213.42
Variance0.200088314
MonotonicityNot monotonic
2022-04-04T19:14:21.557209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017329
68.8%
16941
27.6%
0.5122
 
0.5%
0.0753
 
0.2%
0.0650
 
0.2%
0.0550
 
0.2%
0.3348
 
0.2%
0.0146
 
0.2%
0.0846
 
0.2%
0.2543
 
0.2%
Other values (60)464
 
1.8%
ValueCountFrequency (%)
017329
68.8%
0.0146
 
0.2%
0.0216
 
0.1%
0.0331
 
0.1%
0.0429
 
0.1%
0.0550
 
0.2%
0.0650
 
0.2%
0.0753
 
0.2%
0.0846
 
0.2%
0.0938
 
0.2%
ValueCountFrequency (%)
16941
27.6%
0.9941
 
0.2%
0.9812
 
< 0.1%
0.9716
 
0.1%
0.967
 
< 0.1%
0.956
 
< 0.1%
0.942
 
< 0.1%
0.936
 
< 0.1%
0.922
 
< 0.1%
0.911
 
< 0.1%

srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2837623055
Minimum0
Maximum1
Zeros17708
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:21.807604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4475989077
Coefficient of variation (CV)1.577372678
Kurtosis-1.057887633
Mean0.2837623055
Median Absolute Deviation (MAD)0
Skewness0.9634997182
Sum7148.54
Variance0.2003447822
MonotonicityNot monotonic
2022-04-04T19:14:22.050665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017708
70.3%
17003
 
27.8%
0.594
 
0.4%
0.3351
 
0.2%
0.2542
 
0.2%
0.232
 
0.1%
0.0526
 
0.1%
0.1722
 
0.1%
0.0320
 
0.1%
0.0420
 
0.1%
Other values (46)174
 
0.7%
ValueCountFrequency (%)
017708
70.3%
0.011
 
< 0.1%
0.0213
 
0.1%
0.0320
 
0.1%
0.0420
 
0.1%
0.0526
 
0.1%
0.0610
 
< 0.1%
0.0716
 
0.1%
0.0810
 
< 0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
17003
27.8%
0.959
 
< 0.1%
0.941
 
< 0.1%
0.931
 
< 0.1%
0.923
 
< 0.1%
0.913
 
< 0.1%
0.94
 
< 0.1%
0.893
 
< 0.1%
0.881
 
< 0.1%
0.861
 
< 0.1%

rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1186301207
Minimum0
Maximum1
Zeros21985
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:22.284837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3187454708
Coefficient of variation (CV)2.686884823
Kurtosis3.547199001
Mean0.1186301207
Median Absolute Deviation (MAD)0
Skewness2.346358337
Sum2988.53
Variance0.1015986751
MonotonicityNot monotonic
2022-04-04T19:14:22.516664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021985
87.3%
12552
 
10.1%
0.943
 
0.2%
0.8939
 
0.2%
0.9138
 
0.2%
0.9238
 
0.2%
0.9537
 
0.1%
0.536
 
0.1%
0.9334
 
0.1%
0.9431
 
0.1%
Other values (62)359
 
1.4%
ValueCountFrequency (%)
021985
87.3%
0.018
 
< 0.1%
0.0215
 
0.1%
0.0321
 
0.1%
0.049
 
< 0.1%
0.058
 
< 0.1%
0.062
 
< 0.1%
0.078
 
< 0.1%
0.085
 
< 0.1%
0.091
 
< 0.1%
ValueCountFrequency (%)
12552
10.1%
0.996
 
< 0.1%
0.982
 
< 0.1%
0.977
 
< 0.1%
0.9611
 
< 0.1%
0.9537
 
0.1%
0.9431
 
0.1%
0.9334
 
0.1%
0.9238
 
0.2%
0.9138
 
0.2%

srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1202604001
Minimum0
Maximum1
Zeros21959
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:22.722729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3223353519
Coefficient of variation (CV)2.680311653
Kurtosis3.513875405
Mean0.1202604001
Median Absolute Deviation (MAD)0
Skewness2.340787023
Sum3029.6
Variance0.1039000791
MonotonicityNot monotonic
2022-04-04T19:14:22.927957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
021959
87.2%
12937
 
11.7%
0.556
 
0.2%
0.3332
 
0.1%
0.2526
 
0.1%
0.1717
 
0.1%
0.216
 
0.1%
0.0414
 
0.1%
0.0811
 
< 0.1%
0.6711
 
< 0.1%
Other values (32)113
 
0.4%
ValueCountFrequency (%)
021959
87.2%
0.029
 
< 0.1%
0.038
 
< 0.1%
0.0414
 
0.1%
0.058
 
< 0.1%
0.0610
 
< 0.1%
0.077
 
< 0.1%
0.0811
 
< 0.1%
0.092
 
< 0.1%
0.18
 
< 0.1%
ValueCountFrequency (%)
12937
11.7%
0.851
 
< 0.1%
0.841
 
< 0.1%
0.833
 
< 0.1%
0.812
 
< 0.1%
0.83
 
< 0.1%
0.792
 
< 0.1%
0.761
 
< 0.1%
0.755
 
< 0.1%
0.741
 
< 0.1%

same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct97
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6605589076
Minimum0
Maximum1
Zeros543
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:23.135963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.4396373803
Coefficient of variation (CV)0.6655536324
Kurtosis-1.611734832
Mean0.6605589076
Median Absolute Deviation (MAD)0
Skewness-0.5704892479
Sum16640.8
Variance0.1932810262
MonotonicityNot monotonic
2022-04-04T19:14:23.407325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115357
61.0%
0.01827
 
3.3%
0.02710
 
2.8%
0.06681
 
2.7%
0.03678
 
2.7%
0.07671
 
2.7%
0.04658
 
2.6%
0.08601
 
2.4%
0.05590
 
2.3%
0543
 
2.2%
Other values (87)3876
 
15.4%
ValueCountFrequency (%)
0543
2.2%
0.01827
3.3%
0.02710
2.8%
0.03678
2.7%
0.04658
2.6%
0.05590
2.3%
0.06681
2.7%
0.07671
2.7%
0.08601
2.4%
0.09399
1.6%
ValueCountFrequency (%)
115357
61.0%
0.99147
 
0.6%
0.9818
 
0.1%
0.979
 
< 0.1%
0.964
 
< 0.1%
0.953
 
< 0.1%
0.943
 
< 0.1%
0.939
 
< 0.1%
0.926
 
< 0.1%
0.912
 
< 0.1%

diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06236305176
Minimum0
Maximum1
Zeros15245
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:23.610957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1785499962
Coefficient of variation (CV)2.863073424
Kurtosis19.29583341
Mean0.06236305176
Median Absolute Deviation (MAD)0
Skewness4.417749091
Sum1571.05
Variance0.03188010113
MonotonicityNot monotonic
2022-04-04T19:14:23.815907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015245
60.5%
0.063861
 
15.3%
0.071947
 
7.7%
0.051350
 
5.4%
1663
 
2.6%
0.08374
 
1.5%
0.01194
 
0.8%
0.04130
 
0.5%
0.09121
 
0.5%
0.5116
 
0.5%
Other values (69)1191
 
4.7%
ValueCountFrequency (%)
015245
60.5%
0.01194
 
0.8%
0.0249
 
0.2%
0.0348
 
0.2%
0.04130
 
0.5%
0.051350
 
5.4%
0.063861
 
15.3%
0.071947
 
7.7%
0.08374
 
1.5%
0.09121
 
0.5%
ValueCountFrequency (%)
1663
2.6%
0.9910
 
< 0.1%
0.982
 
< 0.1%
0.971
 
< 0.1%
0.968
 
< 0.1%
0.9512
 
< 0.1%
0.832
 
< 0.1%
0.821
 
< 0.1%
0.82
 
< 0.1%
0.791
 
< 0.1%

srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09593085106
Minimum0
Maximum1
Zeros19517
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:24.022085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2565828469
Coefficient of variation (CV)2.674664553
Kurtosis7.010158078
Mean0.09593085106
Median Absolute Deviation (MAD)0
Skewness2.885942113
Sum2416.69
Variance0.06583475732
MonotonicityNot monotonic
2022-04-04T19:14:24.239012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019517
77.5%
11559
 
6.2%
0.01586
 
2.3%
0.67210
 
0.8%
0.5193
 
0.8%
0.12170
 
0.7%
0.33167
 
0.7%
0.25164
 
0.7%
0.02153
 
0.6%
0.11143
 
0.6%
Other values (47)2330
 
9.2%
ValueCountFrequency (%)
019517
77.5%
0.01586
 
2.3%
0.02153
 
0.6%
0.0340
 
0.2%
0.0439
 
0.2%
0.0559
 
0.2%
0.06118
 
0.5%
0.07111
 
0.4%
0.08124
 
0.5%
0.09112
 
0.4%
ValueCountFrequency (%)
11559
6.2%
0.881
 
< 0.1%
0.831
 
< 0.1%
0.814
 
0.1%
0.7558
 
0.2%
0.713
 
< 0.1%
0.67210
 
0.8%
0.623
 
< 0.1%
0.641
 
0.2%
0.577
 
< 0.1%

dst_host_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.5320737
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:24.449791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q184
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)171

Descriptive statistics

Standard deviation98.99389516
Coefficient of variation (CV)0.5423369886
Kurtosis-1.044780944
Mean182.5320737
Median Absolute Deviation (MAD)0
Skewness-0.8431608128
Sum4598348
Variance9799.791278
MonotonicityNot monotonic
2022-04-04T19:14:24.686322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25514850
58.9%
1601
 
2.4%
2554
 
2.2%
3251
 
1.0%
4241
 
1.0%
5162
 
0.6%
6157
 
0.6%
8134
 
0.5%
10112
 
0.4%
11111
 
0.4%
Other values (246)8019
31.8%
ValueCountFrequency (%)
01
 
< 0.1%
1601
2.4%
2554
2.2%
3251
1.0%
4241
1.0%
5162
 
0.6%
6157
 
0.6%
7107
 
0.4%
8134
 
0.5%
9110
 
0.4%
ValueCountFrequency (%)
25514850
58.9%
25417
 
0.1%
25317
 
0.1%
25217
 
0.1%
25112
 
< 0.1%
25018
 
0.1%
24915
 
0.1%
24817
 
0.1%
24721
 
0.1%
24623
 
0.1%

dst_host_srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.0630359
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:24.899652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median61
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.6468504
Coefficient of variation (CV)0.9616194245
Kurtosis-1.751039982
Mean115.0630359
Median Absolute Deviation (MAD)59
Skewness0.294305757
Sum2898668
Variance12242.72549
MonotonicityNot monotonic
2022-04-04T19:14:25.111261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2557148
28.4%
11658
 
6.6%
21041
 
4.1%
3556
 
2.2%
4516
 
2.0%
20475
 
1.9%
5464
 
1.8%
6453
 
1.8%
254440
 
1.7%
19433
 
1.7%
Other values (246)12008
47.7%
ValueCountFrequency (%)
01
 
< 0.1%
11658
6.6%
21041
4.1%
3556
 
2.2%
4516
 
2.0%
5464
 
1.8%
6453
 
1.8%
7410
 
1.6%
8426
 
1.7%
9404
 
1.6%
ValueCountFrequency (%)
2557148
28.4%
254440
 
1.7%
25391
 
0.4%
25235
 
0.1%
25181
 
0.3%
25067
 
0.3%
24942
 
0.2%
24844
 
0.2%
24757
 
0.2%
24652
 
0.2%

dst_host_same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5197908066
Minimum0
Maximum1
Zeros1379
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:25.315195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.4489439174
Coefficient of variation (CV)0.86370115
Kurtosis-1.88463264
Mean0.5197908066
Median Absolute Deviation (MAD)0.49
Skewness-0.004023662232
Sum13094.57
Variance0.201550641
MonotonicityNot monotonic
2022-04-04T19:14:25.519739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19758
38.7%
0.011541
 
6.1%
01379
 
5.5%
0.021325
 
5.3%
0.071122
 
4.5%
0.041046
 
4.2%
0.051017
 
4.0%
0.03799
 
3.2%
0.06701
 
2.8%
0.08577
 
2.3%
Other values (91)5927
23.5%
ValueCountFrequency (%)
01379
5.5%
0.011541
6.1%
0.021325
5.3%
0.03799
3.2%
0.041046
4.2%
0.051017
4.0%
0.06701
2.8%
0.071122
4.5%
0.08577
 
2.3%
0.09360
 
1.4%
ValueCountFrequency (%)
19758
38.7%
0.99121
 
0.5%
0.98170
 
0.7%
0.97101
 
0.4%
0.96160
 
0.6%
0.95126
 
0.5%
0.9487
 
0.3%
0.9381
 
0.3%
0.9272
 
0.3%
0.9169
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08253850429
Minimum0
Maximum1
Zeros9343
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:25.718843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.1871911135
Coefficient of variation (CV)2.267924712
Kurtosis12.72750928
Mean0.08253850429
Median Absolute Deviation (MAD)0.03
Skewness3.616184988
Sum2079.31
Variance0.03504051299
MonotonicityNot monotonic
2022-04-04T19:14:25.933078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09343
37.1%
0.073448
 
13.7%
0.061917
 
7.6%
0.011881
 
7.5%
0.051436
 
5.7%
0.081367
 
5.4%
0.021327
 
5.3%
0.03745
 
3.0%
0.04603
 
2.4%
0.09523
 
2.1%
Other values (91)2602
 
10.3%
ValueCountFrequency (%)
09343
37.1%
0.011881
 
7.5%
0.021327
 
5.3%
0.03745
 
3.0%
0.04603
 
2.4%
0.051436
 
5.7%
0.061917
 
7.6%
0.073448
 
13.7%
0.081367
 
5.4%
0.09523
 
2.1%
ValueCountFrequency (%)
1408
1.6%
0.997
 
< 0.1%
0.986
 
< 0.1%
0.9718
 
0.1%
0.9612
 
< 0.1%
0.9514
 
0.1%
0.9412
 
< 0.1%
0.933
 
< 0.1%
0.926
 
< 0.1%
0.9128
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1474527628
Minimum0
Maximum1
Zeros12673
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:26.142721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.3083665911
Coefficient of variation (CV)2.091290697
Kurtosis2.810803318
Mean0.1474527628
Median Absolute Deviation (MAD)0
Skewness2.09852676
Sum3714.63
Variance0.0950899545
MonotonicityNot monotonic
2022-04-04T19:14:26.375414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012673
50.3%
0.013557
 
14.1%
12052
 
8.1%
0.021115
 
4.4%
0.03624
 
2.5%
0.04447
 
1.8%
0.05315
 
1.3%
0.5232
 
0.9%
0.08230
 
0.9%
0.06226
 
0.9%
Other values (91)3721
 
14.8%
ValueCountFrequency (%)
012673
50.3%
0.013557
 
14.1%
0.021115
 
4.4%
0.03624
 
2.5%
0.04447
 
1.8%
0.05315
 
1.3%
0.06226
 
0.9%
0.07199
 
0.8%
0.08230
 
0.9%
0.09150
 
0.6%
ValueCountFrequency (%)
12052
8.1%
0.9919
 
0.1%
0.9837
 
0.1%
0.9732
 
0.1%
0.9646
 
0.2%
0.9561
 
0.2%
0.9424
 
0.1%
0.9333
 
0.1%
0.9215
 
0.1%
0.9129
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03184423627
Minimum0
Maximum1
Zeros17387
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:26.614013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.1105749689
Coefficient of variation (CV)3.472369943
Kurtosis36.89909997
Mean0.03184423627
Median Absolute Deviation (MAD)0
Skewness5.617065211
Sum802.22
Variance0.01222682374
MonotonicityNot monotonic
2022-04-04T19:14:26.854674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017387
69.0%
0.021612
 
6.4%
0.011468
 
5.8%
0.03950
 
3.8%
0.04870
 
3.5%
0.05608
 
2.4%
0.5317
 
1.3%
0.06264
 
1.0%
0.07215
 
0.9%
0.25205
 
0.8%
Other values (53)1296
 
5.1%
ValueCountFrequency (%)
017387
69.0%
0.011468
 
5.8%
0.021612
 
6.4%
0.03950
 
3.8%
0.04870
 
3.5%
0.05608
 
2.4%
0.06264
 
1.0%
0.07215
 
0.9%
0.0887
 
0.3%
0.0971
 
0.3%
ValueCountFrequency (%)
1132
0.5%
0.971
 
< 0.1%
0.861
 
< 0.1%
0.81
 
< 0.1%
0.753
 
< 0.1%
0.6716
 
0.1%
0.65
 
< 0.1%
0.575
 
< 0.1%
0.568
 
< 0.1%
0.554
 
< 0.1%

dst_host_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.285800254
Minimum0
Maximum1
Zeros16221
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:27.079489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4453164751
Coefficient of variation (CV)1.558138836
Kurtosis-1.06135891
Mean0.285800254
Median Absolute Deviation (MAD)0
Skewness0.95814722
Sum7199.88
Variance0.198306763
MonotonicityNot monotonic
2022-04-04T19:14:27.290481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016221
64.4%
16739
26.8%
0.01680
 
2.7%
0.02237
 
0.9%
0.03149
 
0.6%
0.0483
 
0.3%
0.0979
 
0.3%
0.0869
 
0.3%
0.0564
 
0.3%
0.9959
 
0.2%
Other values (90)812
 
3.2%
ValueCountFrequency (%)
016221
64.4%
0.01680
 
2.7%
0.02237
 
0.9%
0.03149
 
0.6%
0.0483
 
0.3%
0.0564
 
0.3%
0.0634
 
0.1%
0.0747
 
0.2%
0.0869
 
0.3%
0.0979
 
0.3%
ValueCountFrequency (%)
16739
26.8%
0.9959
 
0.2%
0.9836
 
0.1%
0.9718
 
0.1%
0.9620
 
0.1%
0.9514
 
0.1%
0.9421
 
0.1%
0.9315
 
0.1%
0.9211
 
< 0.1%
0.918
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct88
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2798463798
Minimum0
Maximum1
Zeros17005
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:27.494888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4460753261
Coefficient of variation (CV)1.594000703
Kurtosis-1.021722713
Mean0.2798463798
Median Absolute Deviation (MAD)0
Skewness0.9843387293
Sum7049.89
Variance0.1989831966
MonotonicityNot monotonic
2022-04-04T19:14:27.705104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017005
67.5%
16862
27.2%
0.01758
 
3.0%
0.02136
 
0.5%
0.0332
 
0.1%
0.524
 
0.1%
0.0816
 
0.1%
0.1216
 
0.1%
0.0415
 
0.1%
0.0515
 
0.1%
Other values (78)313
 
1.2%
ValueCountFrequency (%)
017005
67.5%
0.01758
 
3.0%
0.02136
 
0.5%
0.0332
 
0.1%
0.0415
 
0.1%
0.0515
 
0.1%
0.0615
 
0.1%
0.0714
 
0.1%
0.0816
 
0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
16862
27.2%
0.986
 
< 0.1%
0.9713
 
0.1%
0.9613
 
0.1%
0.956
 
< 0.1%
0.948
 
< 0.1%
0.937
 
< 0.1%
0.924
 
< 0.1%
0.919
 
< 0.1%
0.92
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1178000953
Minimum0
Maximum1
Zeros20688
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:27.901987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3058692457
Coefficient of variation (CV)2.596511021
Kurtosis3.765595798
Mean0.1178000953
Median Absolute Deviation (MAD)0
Skewness2.363706744
Sum2967.62
Variance0.09355599545
MonotonicityNot monotonic
2022-04-04T19:14:28.102658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020688
82.1%
12069
 
8.2%
0.01359
 
1.4%
0.02232
 
0.9%
0.03110
 
0.4%
0.0585
 
0.3%
0.0477
 
0.3%
0.9252
 
0.2%
0.946
 
0.2%
0.9143
 
0.2%
Other values (91)1431
 
5.7%
ValueCountFrequency (%)
020688
82.1%
0.01359
 
1.4%
0.02232
 
0.9%
0.03110
 
0.4%
0.0477
 
0.3%
0.0585
 
0.3%
0.0633
 
0.1%
0.0739
 
0.2%
0.0837
 
0.1%
0.0915
 
0.1%
ValueCountFrequency (%)
12069
8.2%
0.997
 
< 0.1%
0.9812
 
< 0.1%
0.9720
 
0.1%
0.9639
 
0.2%
0.9529
 
0.1%
0.9423
 
0.1%
0.9323
 
0.1%
0.9252
 
0.2%
0.9143
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1187694506
Minimum0
Maximum1
Zeros21349
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2022-04-04T19:14:28.300188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3173334682
Coefficient of variation (CV)2.671844204
Kurtosis3.633093022
Mean0.1187694506
Median Absolute Deviation (MAD)0
Skewness2.360483562
Sum2992.04
Variance0.1007005301
MonotonicityNot monotonic
2022-04-04T19:14:28.492887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021349
84.7%
12617
 
10.4%
0.01253
 
1.0%
0.02124
 
0.5%
0.0378
 
0.3%
0.0469
 
0.3%
0.0563
 
0.3%
0.0636
 
0.1%
0.9933
 
0.1%
0.9830
 
0.1%
Other values (90)540
 
2.1%
ValueCountFrequency (%)
021349
84.7%
0.01253
 
1.0%
0.02124
 
0.5%
0.0378
 
0.3%
0.0469
 
0.3%
0.0563
 
0.3%
0.0636
 
0.1%
0.0717
 
0.1%
0.0818
 
0.1%
0.0911
 
< 0.1%
ValueCountFrequency (%)
12617
10.4%
0.9933
 
0.1%
0.9830
 
0.1%
0.9719
 
0.1%
0.9617
 
0.1%
0.9511
 
< 0.1%
0.9411
 
< 0.1%
0.9310
 
< 0.1%
0.927
 
< 0.1%
0.917
 
< 0.1%

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
normal
13449 
anomaly
11743 

Length

Max length7
Median length6
Mean length6.466140044
Min length6

Characters and Unicode

Total characters162895
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rowanomaly
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal13449
53.4%
anomaly11743
46.6%

Length

2022-04-04T19:14:28.807931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T19:14:28.903879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
normal13449
53.4%
anomaly11743
46.6%

Most occurring characters

ValueCountFrequency (%)
a36935
22.7%
n25192
15.5%
o25192
15.5%
m25192
15.5%
l25192
15.5%
r13449
 
8.3%
y11743
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter162895
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a36935
22.7%
n25192
15.5%
o25192
15.5%
m25192
15.5%
l25192
15.5%
r13449
 
8.3%
y11743
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin162895
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a36935
22.7%
n25192
15.5%
o25192
15.5%
m25192
15.5%
l25192
15.5%
r13449
 
8.3%
y11743
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII162895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a36935
22.7%
n25192
15.5%
o25192
15.5%
m25192
15.5%
l25192
15.5%
r13449
 
8.3%
y11743
 
7.2%

Interactions

2022-04-04T19:11:55.412798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:55.592201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:55.755652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.020610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.184918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.338888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.492704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.653496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.813992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:56.962586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.111740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.262595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.414230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.565352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.717482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:57.869737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.015120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.160911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.305346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.451545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.599430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.746729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:58.893743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.039812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.186175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.332609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.480257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.625793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:11:59.887582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:00.069005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:00.254658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:00.457099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:00.639785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:00.827732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.010722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.200561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.381731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.581760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.764244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:01.946220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:02.133717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:02.301265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:02.512332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:02.685052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:02.851696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.012737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.167234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.332356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.511383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.678882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:03.852089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.020276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.179563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.348140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.523163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.663056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:04.935037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.085731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.250498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.393625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.533562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.685342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.844732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:05.988716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.138780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.313588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.460727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.602816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.752114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:06.919846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.068325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.218219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.363202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.510875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.659059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.807183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:07.955845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.104211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.252840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.401092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.551523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.699536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:08.851489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.020350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.177206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.341557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.498229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.654143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.819666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:09.985007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:10.139713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:10.296765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:10.615800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:10.774257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:10.930709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.090797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.252489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.415523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.573133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.729781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:11.886721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.046751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.205410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.366922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.527205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.684910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:12.843261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.000735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.159348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.295781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.446504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.585181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.731342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:13.866228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.004370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.153012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.303061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.445090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.586848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.727855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:14.866995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:15.005943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:15.145658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:15.288998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:15.429284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:12:15.569612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-04-04T19:14:08.381564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:14:08.558499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:14:08.732709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:14:08.899660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:14:09.067930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-04T19:14:09.927107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-04T19:14:29.101947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-04T19:14:29.700045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-04T19:14:30.292084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-04T19:14:30.891817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-04T19:14:31.390546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-04T19:14:10.429150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-04T19:14:12.288843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
00tcpftp_dataSF49100000000000000000220.00.00.00.01.000.000.00150250.170.030.170.000.000.000.050.00normal
10udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.000.00normal
20tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.000.00anomaly
30tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.000.01normal
40tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.000.00normal
50tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.001.00anomaly
60tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.000.00anomaly
70tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.000.00anomaly
80tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.000.00anomaly
90tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.000.00anomaly

Last rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
251820tcpotherREJ00000000000000000051110.120.000.851.00.001.000.0025510.001.000.000.000.160.00.821.0anomaly
251830tcpprivateREJ00000000000000000031410.030.000.951.00.001.000.0025510.001.000.000.000.040.00.961.0anomaly
2518429tcpftpSF32910630006010000000001110.000.000.000.01.000.000.00255600.240.020.000.000.000.00.030.1normal
251851tcpsmtpSF28963330000010000000000130.000.000.000.01.000.001.0012110.920.170.080.000.000.00.000.0normal
251860tcphttpS13391460000000100000000002330.500.030.000.01.000.000.061732551.000.000.010.010.010.00.010.0normal
251870tcpexecRSTO00000000000000000010070.000.001.001.00.070.070.0025570.030.060.000.000.000.01.001.0anomaly
251880tcpftp_dataSF33400000010000000000110.000.000.000.01.000.000.001391.000.001.000.180.000.00.000.0anomaly
251890tcpprivateREJ00000000000000000010570.000.001.001.00.070.070.00255130.050.070.000.000.000.01.001.0anomaly
251900tcpnnspS0000000000000000000129181.001.000.000.00.140.060.00255200.080.060.000.001.001.00.000.0anomaly
251910tcpfingerS00000000000000000003891.001.000.000.00.240.110.00255490.190.030.010.001.001.00.000.0anomaly